Intelligent Agents
Chapter 2 – AIMA
Dr. Aarij Mahmood Hussaan
What is an (Intelligent) Agent?
• An over-used, over-loaded, and misused term.
• Anything that can be viewed as perceiving its environment through
sensors and acting upon that environment through its actuators to
maximize progress towards its goals.
What is an (Intelligent) Agent?
• PAGE
(Percepts, Actions, Goals, Environment)
• PEAS[AIMA]
(Performance, Environment, Actuators, Sensors)
• Task-specific & specialized: well-defined goals and environment
• The notion of an agent is meant to be a tool for analyzing systems,
• It is not a different hardware or new programming languages
Agent
• Percept
• agent’s perceptual inputs at any given instant
• Percept Sequence
• the complete history of everything the agent has ever perceived
• Agent’s action depends upon the complete history of percept
sequence to date
Agents and environments
• Agents include humans, robots, softbots, thermostats, etc.
• The agent function maps from percept histories to actions:
f : P*  A
• The agent program runs on the physical architecture to produce f
Vacuum-cleaner world
• Percepts: location and contents, e.g., [A;Dirty]
• Actions: Left, Right, Suck, NoOp
A vacuum-cleaner agent
function Reflex-Vacuum-Agent( [location,status]) returns an action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
What is the right function?
Can it be implemented in a small agent program?
Rationality
Fixed performance measure evaluates the environment sequence
• one point per square cleaned up in time T?
• one point per clean square per time step, minus one per move?
• penalize for > k dirty squares?
A rational agent chooses whichever action maximizes the expected value of the
performance measure given the percept sequence to date
Rational != omniscient
• percepts may not supply all relevant information
Rational != clairvoyant
• action outcomes may not be as expected
Hence, rational != successful
Rational => exploration, learning, autonomy
Agent
To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing an automated taxi:
Performance measure??
Environment??
Actuators??
Sensors??
Agent
To design a rational agent, we must specify the task environment
Consider, e.g., the task of designing an automated taxi:
Performance measure?? safety, destination, profits, legality, comfort, ..
Environment?? streets/highways, tracks, pedestrians, weather,
Actuators?? steering, accelerator, brake, horn, speaker/display,….
Sensors?? video, accelerometers, gauges, engine sensors, keyboard, GPS, :…
Some more examples
A Windshield Wiper Agent
Goals:?
Percepts:?
Sensors:?
Actuators: ?
Actions:?
Environment: ?
A Windshield Wiper Agent
Goals: Keep windshields clean & maintain visibility
Percepts: Raining, Dirty
Sensors: Camera (moist sensor)
Actuators: Wipers (left, right, back)
Actions: Off, Slow, Medium, Fast
Environment: Inner city, freeways, highways, weather …
Internet shopping agent
Performance measure?? price, quality, appropriateness, efficiency
Environment?? current and future WWW sites, vendors, shippers
Actuators?? display to user, follow URL, fill in form
Sensors?? HTML pages (text, graphics, scripts)
Interacting Agents
Collision Avoidance Agent (CAA)
Goals : Avoid running into obstacles
Percepts: ?
Sensors:?
Actuators:?
Actions: ?
Environment: Freeway
Lane Keeping Agent (LKA)
•Goals : Stay in current lane
•Percepts:?
•Sensors:?
•Actuators:?
•Actions:?
•Environment: Freeway
Interacting Agents
Collision Avoidance Agent (CAA)
Goals : Avoid running into obstacles
Percepts: Obstacle distance, velocity, trajectory
Sensors: Vision, proximity sensing
Actuators : Steering Wheel, Accelerator, Brakes, Horn, Headlights
Actions : Steer, speed up, brake, blow horn, signal (headlights)
Environment: Freeway
Lane Keeping Agent (LKA)
•Goals : Stay in current lane
•Percepts: Lane center, lane boundaries
•Sensors: Vision
•Actuators: Steering Wheel, Accelerator, Brakes
•Actions: Steer, speed up, brake
•Environment: Freeway
Conflict Resolution by Action Selection Agents
• Override: CAA overrides LKA
• Arbitrate: if Obstacle is Close then CAA else LKA
• Compromise: Choose action that satisfies both agents
• Any combination of the above
• Challenges: Doing the right thing
Behavior and performance of IAs
Perception(sequence) to Action Mapping: f : P*  A
◦Ideal mapping: specifies which actions an agent ought to take at
any point in time
◦Description: Look-Up-Table, Closed Form, etc.
Performance measure: a subjective measure to characterize how
successful an agent is (e.g., speed, power usage, accuracy, money, etc.)
(degree of) Autonomy: to what extent is the agent able to make
decisions and take actions on its own?
Look up table
Closed form
• Output (degree of rotation) = F(distance)
• E.g., F(d) = 10/d (distance cannot be less than 1/10)
How is an Agent different from other
software?
• Agents are autonomous, that is, they act on behalf of the user
• Agents contain some level of intelligence, from fixed rules to learning
engines that allow them to adapt to changes in the environment
• Agents don't only act reactively, but sometimes also proactively
• Agents have social ability, that is, they communicate with the user,
the system, and other agents as required
• Agents may also cooperate with other agents to carry out more
complex tasks than they themselves can handle
• Agents may migrate from one system to another to access remote
resources or even to meet other agents
Environment types
• Fully observable vs. partially observable
• Single agent vs. multi-agent
• Deterministic vs. stochastic
• Episodic vs. sequential
• Static vs. dynamic
• Discrete vs. continuous
• (Known vs. unknown)
Environment types
Solitaire
Observable?
Deterministic?
Episodic?
Static?
Discrete?
Single-agent?
Chess
Internet Shopping
Taxi
Environment types
Solitaire
Observable?
Deterministic?
Episodic?
Static?
Discrete?
Single-agent?
Yes
Chess
Yes
Internet Shopping
No
Taxi
No
Environment types
Solitaire
Chess
Internet Shopping
Taxi
Observable?
Yes
Yes
No
No
Deterministic?
Yes
No
Partly
No
Episodic?
Static?
Discrete?
Single-agent?
Environment types
Solitaire
Chess
Internet Shopping
Taxi
Observable?
Yes
Yes
No
No
Deterministic?
Yes
No
Partly
No
Episodic?
No
No
No
No
Static?
Discrete?
Single-agent?
Environment types
Solitaire
Chess
Internet Shopping
Taxi
Observable?
Yes
Yes
No
No
Deterministic?
Yes
No
Partly
No
Episodic?
No
No
No
No
Static?
Yes
Semi
Semi
No
Discrete?
Single-agent?
Environment types
Solitaire
Chess
Internet Shopping
Taxi
Observable?
Yes
Yes
No
No
Deterministic?
Yes
No
Partly
No
Episodic?
No
No
No
No
Static?
Yes
Semi
Semi
No
Discrete?
Yes
Yes
Yes
No
Single-agent?
Environment types
Solitaire
Chess
Internet Shopping
Taxi
Observable?
Yes
Yes
No
No
Deterministic?
Yes
No
Partly
No
Episodic?
No
No
No
No
Static?
Yes
Semi
Semi
No
Discrete?
Yes
Yes
Yes
No
Single-agent?
Yes
No
Yes (except
auctions)
No
The environment type largely determines the agent design
The real world is (of course) partially observable, stochastic, sequential, dynamic, continuous,
multi-agent
Structure of Intelligent Agents
• Agent = architecture + program
• Agent program: the implementation of f : P*  A, the agent’s
perception-action mapping
Function Skeleton-Agent(Percept) returns Action
memory  UpdateMemory(memory, Percept)
Action  ChooseBestAction(memory)
memory  UpdateMemory(memory, Action)
Return Action
• Architecture: a device that can execute the agent program (e.g.,
general-purpose computer, specialized device, beobot, etc.)
Using a look-up-table to encode f : P*  A
• Example: Collision Avoidance
◦Sensors:3 proximity sensors
◦Effectors: Steering Wheel, Brakes
• How to generate?
• How large?
• How to select action?
Using a look-up-table to encode f : P*  A
• Example: Collision Avoidance
◦Sensors: 3 proximity sensors
◦Effectors: Steering Wheel, Brakes
• How to generate: for each p ∈ Pl x Pm x Pr
generate an appropriate action, a ∈ S x B
• How large: size of table = #possible percepts * #possible actions = |Pl
| |Pm| |Pr| |S| |B|
E.g., P = {close, medium, far}3 A = {left, straight, right} {on, off}
then size of table = 27*3*2 = 162
• How to select action? Search.
Agent types
• Four basic types in order of increasing generality:
•
•
•
•
simple reflex agents
reflex agents with state
goal-based agents
utility-based agents
• All these can be turned into learning agents
Simple reflex agents
Simple reflex agents
function Reflex-Vacuum-Agent( [location, status]) returns an action
if status = Dirty then return Suck
else if location = A then return Right
else if location = B then return Left
Reflex agents with state
Reflex agents with state
Goal-based agents
Utility-based agents
Learning agents
Summary
Agents interact with environments through actuators and sensors
The agent function describes what the agent does in all circumstances
The performance measure evaluates the environment sequence
A perfectly rational agent maximizes expected performance
Agent programs implement (some) agent functions
PEAS descriptions define task environments
Environments are categorized along several dimensions:
• observable? deterministic? episodic? static? discrete? single-agent?
Several basic agent architectures exist:
• reflex, reflex with state, goal-based, utility-based